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machine-learning-pipeline

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Explore a collection of Jupyter notebooks that guide you through various stages of the machine learning pipeline. From data analysis and feature engineering to model training and deployment, these notebooks provide practical insights for both beginners and experienced data enthusiasts. Let's dive into the world of data-driven decision-making! 📊🚀"

  • Updated Aug 29, 2023
  • Jupyter Notebook

This repo showcases a project that transforms ML model training into a simplified, production-ready Kedro Dockerized Pipeline. It emphasizes best MLOps practices, enabling easy training, evaluation, and deployment of models, including XGBoost, LightGBM and Random Forest, with built-in visualization and logging features for effective monitoring.

  • Updated Apr 5, 2024
  • Jupyter Notebook

The code snippet cleans and analyzes a hotel bookings dataset, handling missing values, dropping unnecessary columns, and creating new features. It visualizes the data using various plots and performs feature encoding and selection. It then trains machine learning models to predict hotel booking cancellations.

  • Updated Jul 5, 2023
  • Jupyter Notebook

This repository shows the implementation of machine learning algorithms, data pipelines and data visualization with scikit-learn and python.

  • Updated Jul 16, 2021
  • Jupyter Notebook

An advanced MLOps project featuring an end-to-end machine learning pipeline with a Random Forest Classifier. This repository automates data preprocessing, model training, hyperparameter tuning, and deployment using CI/CD, containerization, and cloud deployment. It includes real-time model monitoring, data versioning with DVC (Data Version Control)

  • Updated Oct 5, 2024
  • Jupyter Notebook

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